Milvus is an open-source vector database designed for high-speed similarity search and retrieval of large-scale embedding data. Developed by Zilliz and built for AI-native workloads, it’s widely used in scenarios such as semantic search, image retrieval, recommendation engines, and video analysis.

Milvus is suitable for data scientists, AI/ML engineers, and backend developers working in industries like e-commerce, finance, security, and autonomous systems. It supports billions of vectors, integrates with major ML frameworks, and offers advanced indexing options for optimized search performance.
Key benefits include:

• High-throughput vector search at low latency

• Multiple indexing and distance metric options

• Scalable, modular, and cloud-native architecture

What are the main features of Milvus?

High-performance vector indexing

Milvus supports multiple indexing algorithms tailored for different use cases and dataset sizes.

• Index types include IVF, HNSW, ANNOY, and Flat

• Supports cosine, Euclidean (L2), and inner product metrics

• Dynamic indexing with update and delete capabilities

Horizontal scalability

Milvus is designed to handle massive datasets with ease, scaling out across nodes as needed.

• Supports distributed architecture with separation of compute and storage

• Efficient resource management using Milvus’ custom query node design

• Easily scales to billions of vectors

Flexible deployment options

Milvus can be deployed in various environments to suit different infrastructure requirements.

• Self-hosted via Docker or Kubernetes

• Fully managed via Zilliz Cloud

• Integrates with object storage systems (e.g., S3, MinIO)

Integration with machine learning tools

Milvus fits seamlessly into ML pipelines and supports vector ingestion from popular frameworks.

• Works with TensorFlow, PyTorch, Hugging Face, and OpenAI embeddings

• APIs and SDKs available in Python, Go, Java, C++, and Node.js

• Compatible with common data science tools and workflows

Rich query capabilities

Milvus provides versatile search and filtering mechanisms for more targeted results.

• Vector search by similarity with top-k results

• Boolean filtering on vector metadata

• Range and term queries for hybrid search scenarios

Why choose Milvus?

• Optimized for AI search workloads: Designed from the ground up for handling embeddings from modern deep learning models.

• Highly scalable and distributed: Efficient architecture enables scaling across billions of vectors and multiple nodes.

• Flexible and pluggable indexing: Choose the indexing strategy best suited to your latency and accuracy requirements.

• Broad integration with ML ecosystems: Compatible with the most widely used AI/ML frameworks and tools.

• Mature open-source project: Backed by Zilliz and a growing global community, with active development and strong documentation.